Overview

Kimi K2.7 Code delivers exceptional performance for complex reasoning and code generation tasks within the LLM Resayil platform. Built on a 1t parameter architecture with int4 quantization, this thinking model balances high level cognition with efficient inference speeds. Developers gain immediate access to a massive 262,144 token context window, enabling seamless analysis of extensive codebases or documentation without fragmentation. Integration is streamlined for rapid deployment, allowing API builders to execute their first call within minutes using our standardized endpoints. The model requires a pro tier account and operates at an 8x credit multiplier, ensuring resources are allocated to high value computational tasks.

For researchers and enterprise leaders, Kimi K2.7 Code offers robust bilingual proficiency, excelling in both Arabic and English benchmarks compared to alternative models in its class. This capability ensures accurate processing of regional data streams alongside global technical standards. Production readiness is confirmed through rigorous testing, making it suitable for critical pipelines requiring strict reliability. Transparent pricing structures are available directly within the dashboard, supporting major regional currencies including KWD, SAR, and AED without requiring sales consultations. This combination of advanced reasoning, extensive context handling, and flexible financial options makes it the optimal choice for scaling intelligent applications across diverse linguistic environments.

Specifications

Display Name Kimi K2.7 Code

Family Kimi

Category Thinking

Parameters 1t

Context Window 262,144 tokens

Quantization int4

Min Tier Pro

Status Available

Pricing

8×

credits per token

1K 8,000 Credits

10K 80,000 Credits

100K 800,000 Credits

View Pricing Plans

Code Examples

from openai import OpenAI

client = OpenAI(
    base_url="https://llmapi.resayil.io/v1/",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="kimi-k2.7-code",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

import anthropic

client = anthropic.Anthropic(
    base_url="https://llmapi.resayil.io/v1",
    api_key="YOUR_API_KEY"
)

message = client.messages.create(
    model="kimi-k2.7-code",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(message.content[0].text)

const response = await fetch(
    "https://llmapi.resayil.io/v1/chat/completions",
    {
        method: "POST",
        headers: {
            "Content-Type": "application/json",
            "Authorization": "Bearer YOUR_API_KEY"
        },
        body: JSON.stringify({
            model: "kimi-k2.7-code",
            messages: [
                { role: "user", content: "Hello!" }
            ]
        })
    }
);

const data = await response.json();
console.log(data.choices[0].message.content);

curl https://llmapi.resayil.io/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "kimi-k2.7-code",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Analyzing entire repository codebases for refactoring opportunities

Generating complex unit tests for legacy systems

Debugging distributed systems with extensive log files

Migrating legacy codebases to modern framework architectures

Reviewing security vulnerabilities across large project files

In-Depth Guide

Full Guide

Complete Guide to Kimi K2.7 Code — LLM Resayil

Kimi K2.7 Code